Show simple item record

dc.contributor.authorLu, Jingen
dc.contributor.authorKeech, Malcolmen
dc.contributor.authorWang, Cuiqingen
dc.date.accessioned2014-11-11T12:46:35Z
dc.date.available2014-11-11T12:46:35Z
dc.date.issued2014-09
dc.identifier.citationLu, J., Keech, M., Wang, C., (2014) 'Protein Data Modelling for Concurrent Sequential Patterns' 5th International Workshop on Biological Knowledge Discovery and Data Mining, Munich 3rd September.en
dc.identifier.urihttp://hdl.handle.net/10547/334492
dc.description.abstractProtein sequences from the same family typically share common patterns which imply their structural function and biological relationship. The challenge of identifying protein motifs is often addressed through mining frequent itemsets and sequential patterns, where post-processing is a useful technique. Earlier work has shown that Concurrent Sequential Patterns mining can be applied in bioinformatics, e.g. to detect frequently occurring concurrent protein sub-sequences. This paper presents a companion approach to data modelling and visualisation, applying it to real-world protein datasets from the PROSITE and NCBI databases. The results show the potential for graph-based modelling in representing the integration of higher level patterns common to all or nearly all of the protein sequences.
dc.language.isoenen
dc.publisherDEXAen
dc.relation.urlhttp://www.dexa.org/previous/dexa2014/ws_program387a.html?cid=439en
dc.subjectprotein sequencesen
dc.subjectdata miningen
dc.subjectconcurrent sequential patterns (ConSP)en
dc.subjectbioinformaticsen
dc.subjectConSP modellingen
dc.subjectbiological databasesen
dc.subjectknowledge representationen
dc.subjectvisualizationen
dc.titleProtein data modelling for concurrent sequential patternsen
dc.typeConference papers, meetings and proceedingsen
dc.contributor.departmentUniversity of Bedfordshireen
html.description.abstractProtein sequences from the same family typically share common patterns which imply their structural function and biological relationship. The challenge of identifying protein motifs is often addressed through mining frequent itemsets and sequential patterns, where post-processing is a useful technique. Earlier work has shown that Concurrent Sequential Patterns mining can be applied in bioinformatics, e.g. to detect frequently occurring concurrent protein sub-sequences. This paper presents a companion approach to data modelling and visualisation, applying it to real-world protein datasets from the PROSITE and NCBI databases. The results show the potential for graph-based modelling in representing the integration of higher level patterns common to all or nearly all of the protein sequences.


This item appears in the following Collection(s)

Show simple item record